As
previously discussed, face recognition using 2D images is sensitive to
illumination changes. The light collected from a face is a function of the
geometry of the face, the albedo of the face, the properties of the light
source and the properties of the camera. Given this complexity, it is
difficult to develop models that take all these variations into account.
Training using different illumination scenarios as well as illumination
normalization of 2D images has been used, but with limited success. In 3D
images, variations in illumination only affect the texture of the face,
yet the captured facial shape remains intact

Another differentiating factor between 2D and 3D face recognition is the
effect of pose variation. In 2D images effort has been put into
transforming an image into a canonical position. However, this relies on
accurate landmark placement and does not tackle the issue of occlusion.
Moreover, in 2D this task is nearly impossible use to the projective
nature of 2D images. To circumvent this problem it is possible to ore
different views of the face. This, however, requires a large number of 2D
images from many different views to be collected. An alternative approach
to address the pose variation problem in 2D images is either based on
statistical models for view interpolation or on the use of generative
models. Other strategies including sampling the plenoptic function of a
face using lightfield techniques. Using 3D images, this view interpolation
can be simply solved by re-rendering the 3D face data with a new pose.
This allows a 3Dmorphable model to estimate the 3D shape of unseen faces
from non-frontal 2D input images and to generate 2D frontal views of the
reconstructed faces by re-rendering. Another pose-related problem is that
the physical dimensions of the face in 2D images are unknown. The size of
a face in 2D images is essentially a function of the distance of the
subject from the sensor. However, in 3D images the physical dimensions of
the face are known and are inherently encoded in the data. In contrast to
2D images, 3D images are better at capturing the surface geometry of the
face. Traditional 2D image-based face recognition focuses on high-contrast
areas of the face such as eyes, mouth, nose and face boundary because low
contrast areas such as the jaw and cheeks are difficult to describe from
intensity images. 3D images, on the other hand, make no distinction
between high- and low-contrast areas. 3D face recognition, however, is not
without its problems. Illumination, for example, may not be an issue
during the processing of 3D data, but it is still a problem during
capturing. Depending on the sensor technology used, oily parts of the face
with high reflectance may introduce artifacts under certain lighting on
the surface. The overall quality of 3D image data collected using a range
camera is perhaps not as reliable as 2D image data, because 3D sensor
technology is currently not as mature as 2D sensors. Another disadvantage
of 3D face recognition techniques is the cost of the hardware. 3D
capturing equipment is getting cheaper and more widely available but its
price is significantly higher compared to a high resolution digital
camera. Moreover, the current computational cost of processing 3D data is
higher than for 2D data.

Finally, one of the most important disadvantages of 3D face recognition is
the fact that 3D capturing technology requires cooperation from a subject.
As mentioned above, lens or laserbased scanners require the subject to be
at a certain distance from the sensor. Furthermore, a laser scanner
requires a few seconds of complete immobility, while a traditional camera
can capture images from far away with no cooperation from the subjects. In
addition, there are currently very few high-quality 3D face databases
available for testing and evaluation purposes. Those databases that are
available are of very small size compared to 2D face databases used for
benchmarking.

The
comparison of different 3D face recognition techniques is very challenging
for a number of reasons: Firstly, there are very few standardized 3D face
databases which are used for benchmarking purposes. Thus, the size and
type of 3D face datasets varies significantly across different
publications. Secondly, there are differences in the experimental setup
and in the metrics which are used to evaluate the performance of face
recognition techniques. Table 3.4 gives an overview of the different
methods discussed in the previous section, in terms of the data and
algorithms used and the reported recognition performance. Even though 3D
face recognition is still a new and emerging area, there is a need to
compare the strength of each technique in a controlled setting where they
would be subjected to the same evaluation protocol on a large dataset.
This need for objective evaluation prompted the design of the FRVT 2000
and FRVT 2002 evaluation studies aswell as the upcoming FRVT 2006
(http://www.frvt.org/). Both studies follow the principles of biometric
evaluation laid down in the FERET evaluation strategy (Phillips et al.,
2000). So far, these evaluation studies are limited to 2D face recognition
techniques but will hopefully include 3D face recognition techniques in
the near future.